Gas monitoring data anomaly identification based on spatio-temporal correlativity analysis

Based on spatio-temporal correlativity analysis method, the automatic identification techniques for data anomaly monitoring of coal mining working face gas are presented. The asynchronous correlative characteristics of gas migration in working face airflow direction are qualitatively analyzed. The calculation method of asynchronous correlation delay step and the prediction and inversion formulas of gas concentration changing with time and space after gas emission in the air return roadway are provided. By calculating one hundred and fifty groups of gas sensors data series from a coal mine which have the theoretical correlativity, the correlative coefficient values range of eight kinds of data anomaly is obtained. Then the gas moni- toring data anomaly identification algorithm based on spatio-temporal correlativity analysis is accordingly presented. In order to improve the efficiency of analysis, the gas sensors code rules which can express the spatial topological relations are sug- gested. The experiments indicate that methods presented in this article can effectively compensate the defects of methods based on a single gas sensor monitoring data.

Vector Correlations and Product Rotational Alignments of Reactions Ca+RBr→CaBr+R(R=CH_(3),C_(2) H_(5) and n-C_(3) H_(7))

The quasiclassical trajectory method is used to study the vector correlations of the reactions Ca＋RBr （R=CH3, C2H5 and n-C3H7Br） and the rotational alignment of product CaBr. The product rotational alignment parameters at different collision energies and the vector correlations between the reagent and product are numerically calculated. The vector correlations are described by using the angle distribution functions P（θr）, P（φr）, P（θr, φr） and the polarization-dependent differential cross sections （PDDCSs）. The peak values of P（θr） of the product CaBr from Ca＋CH3Br are larger than those from Ca＋C2H5Br and Ca＋n-C3H7Br. The peak of P（θr） at φr= 3π/2 is apparently stronger than that at φr= x/2 for the three reactions Ca＋RBr. The calculation results show that the rotational angular momentum of the product CaBr is not only aligned, but also oriented along the direction which is perpendicular to the scattering plane.The product CaBr molecules are strongly scattered forward. The orientation and alignment of the product angular momentum will affect the scattering direction of the product molecules to varying degrees.

A Correlation Tracking Algorithm Based on Template Partition Motion Estimation

A correlation tracking algorithm based on template partition motion estimation proposed for improving real time performance of the conventional correlation matching algorithms. The target trajectory fitted using the least square with equal space in whole interval and the target prediction point is found out. According to the requirements of block motion estimation(BME) algorithm,the template divided into some macro blocks. The searching process is conducted by using diamond search algorithm around the prediction point and the optimal motion vector of each block is calculated. A point corresponding to the motion vector with the best matching is taken as a rough matching point of the template. The relation of relative position between the block with matching point and the searching area determined to decide whether to conduct precise matching search or to construct a new search area in the gradient direction. The target tracking experiment results show that over 70% time cost can be reduced caompared with the conventional correlation matching algorithm based on full search method.

Use of Quantum Trajectories in Computational Molecular Bioscience

A spin-dependent quantum trajectory methodology is outlined which achieves electron exchangecorrelation on an ab initio basis. The methodology is intended to give workers in electronic structure the same computational capability which has been available for decades in classical dynamics.

基于知识的目标关系分析挖掘技术

在战场目标价值分析和打击目标排序分析过程中,为了构建敌方作战目标体系,需要分析战场目标间的关联关系。轨迹和部署数据中隐藏大量信息,提出了一种从轨迹和部署数据中挖掘出感兴趣的目标关系类型信息的方法,所提方法对轨迹部署数据进行时空聚类,从聚类结果提取目标;对聚类目标使用频繁项挖掘算法分析挖掘满足一定支持度的有关联关系的目标,再根据构建的关系类型知识库或关系规则,分析目标间的具体关系类型。所提方法能对积累的目标历史轨迹部署数据分析挖掘出目标间的关联关系,挖掘出目标潜在的关系类型可为后续构建目标体系提供关系数据。

徐州市不同类型颗粒物重污染过程特征及组分差异性分析

经过多年的大气污染防治,我国空气质量有了大幅改善,但重污染过程仍有发生。对2018—2021年徐州市3种不同类型颗粒物重污染过程的污染特征、演变趋势、PM_(2.5)组分特征和相关性及污染成因分析结果表明:在不同类型的重污染过程中,二次无机离子NO_(3)^(-)、SO_(4)^(-2)、NH+4在PM_(2.5)中的占比均是最高。在累积型重污染期间,NO_(3)^(-)、SO_(4)^(-2)、NH^(4+)分别增长144%、142%、183%,二次无机离子对PM_(2.5)的增长贡献较大。结合相关性分析及SOR、NOR值发现,硝酸盐和硫酸盐的二次生成作用显著。在沙尘型重污染期间,结合雷达监测结果及后向轨迹图可以看出,沙尘沉降至高空与近地面污染物叠加造成颗粒物高值。化学组分中Ca^(2+)、Mg^(2+)浓度对PM_(2.5)浓度的影响最大,二次生成和转化对其影响较小。在烟花燃放型重污染期间,和烟花爆竹有关的K^(+)、Mg^(2+)、Cl^(-)离子较污染前分别上升1112%、2058%、和239%,对污染过程影响显著。

A new monitoring index for ecological vulnerability and its application in the Yellow River Basin,China from 2000 to 2022

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.

基于时空特征的差分隐私保护方法

为了更好地对轨迹数据中蕴含的个人敏感信息进行保护,采用理论研究与实验证明相结合的方法,在现有的隐私保护框架基础之上进一步考虑时间特性与空间特征之间的关联关系,提出了一种基于时空关联特征的差分隐私保护方法(SC-DP)。根据数据集的局部动态自适应分配时间和空间维度的权重,使得在连续的ε时间窗口以及m×m区域内出现的轨迹子流都能满足ε-差分隐私的要求,并在真实数据集上验证了算法的可用性和实用性,在Geolife数据集上的平均误差降低32.8%,CabSpotting数据集中的性能提升29.2%。

漯河市小麦条锈病传输特征及预测模型研究

基于2010-2020年河南省漯河市条锈病资料、常规气象观测资料和GDAS再分析资料,采用HYSPLIT后向轨迹模型和聚类分析,分析2015-2020年春季小麦条锈病气团孢子传播特征,结果表明,春季河南中部主要有4条典型孢子气团输送路径,其中偏西路径是影响漯河的主要路径,但孢子输送不强。西北、西北转东北及偏东等3条路径的轨迹数及传输孢子数占多数,其输送孢子数能力均强于偏西路径的输送能力,具有重要作用。小麦条锈病的发生与气象条件有直接关系。通过Spearman相关分析筛选出关键气象因子,运用有序Logistic多元回归,建立小麦条锈病发生程度的等级概率预测模型。在2021-2022年预测模型的业务试用中,年发生程度预测结果表明,由2月南风风向频率和3月中旬平均风速组建的双因子模型预测年发生程度等级一致准确率为100%,优于1月平均气温建立的单因子模型的准确率。对4月发生程度的预测,3个模型的等级一致准确率均为50%,相差一级的准确率均为100%。

基于改进核相关滤波的桥吊负载摆角实时检测方法

在基于视觉的桥吊负载摆角检测过程中,负载快速运动使相机采集的图像产生运动模糊,进而导致摆角检测精度降低。为了准确、实时获取负载摆角信息,提出一种基于改进核相关滤波的摆角检测方法。将轨迹预测引入负载跟踪中,避免负载由于快速运动而逃离搜索窗口;设计尺度自适应策略,进一步提高跟踪性能;结合负载运动速度和响应峰值梯度更新自适应学习率,及时适应目标的特征变化。对兴趣区内的图像进行灰度增强处理,降低运动模糊对检测精度的影响,根据三角形外接圆定理获取标识物的圆心及半径,并结合起重机工作空间建立负载摆角测量模型。实验结果表明,所提出方法在检测准确性和实时性上均优于其他方法,检测误差不高于3像素,处理速度不低于35帧/s。

Spatio-temporal correlation between human activity intensity and land surface temperature on the north slope of Tianshan Mountains

Research on the spatio-temporal correlation between the intensity of human activities and the temperature of earth surfaces is of great significance in many aspects,including fully understanding the causes and mechanisms of climate change,actively adapting to climate change,pursuing rational development,and protecting the ecological environment.Taking the north slope of Tianshan Mountains,located in the arid area of northwestern China and extremely sensitive to climate change,as the research area,this study retrieves the surface temperature of the mountain based on MODIS data,while characterizing the intensity of human activities thereby data on the night light,population distribution and land use.The evolution characteristics of human activity intensity and surface temperature in the study area from 2000 to 2018 were analyzed,and the spatio-temporal correlation between them was further explored.It is found that:(1)The average human activity intensity(0.11)in the research area has kept relatively low since this century,and the overall trend has been slowly rising in a stepwise manner(0.0024·a-1);in addition,the increase in human activity intensity has lagged behind that in construction land and population by 1-2 years.(2)The annual average surface temperature in the area is 7.18℃with a pronounced growth.The rate of change(0.02℃·a-1)is about 2.33 times that of the world.The striking boost in spring(0.068℃·a-1)contributes the most to the overall warming trend.Spatially,the surface temperature is low in the south and high in the north,due to the prominent influence of the underlying surface characteristics,such as elevation and vegetation coverage.(3)The intensity of human activity and the surface temperature are remarkably positively correlated in the human activity areas there,showing a strong distribution in the east section and a weak one in the west section.The expression of its spatial differentiation and correlation is comprehensively affected by such factors as scopes of human activities,manifestations,and land-use changes.Vegetation-related human interventions,such as agriculture and forestry planting,urban greening,and afforestation,can effectively reduce the surface warming caused by human activities.This study not only puts forward new ideas to finely portray the intensity of human activities but also offers a scientific reference for regional human-land coordination and overall development.

EcoVis:visual analysis of industrial-level spatio-temporal correlations in electricity consumption

Closely related to the economy,the analysis and management of electricity consumption has been widely studied.Conventional approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative analysis.In this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption data.We not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single map.We implement the system with the cooperation with domain experts.Experiments are conducted to demonstrate the effectiveness of our method.

A NOVEL APPROACH FOR DOA ESTIMATION IN UNKNOWN CORRELATED NOISE FIELDS

The key of the subspace-based Direction Of Arrival (DOA) estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.

HybridTune: Spatio-Temporal Performance Data Correlation for Performance Diagnosis of Big Data Systems

With tremendous growing interests in Big Data, the performance improvement of Big Data systems becomes more and more important. Among many steps, the first one is to analyze and diagnose performance bottlenecks of the Big Data systems. Currently, there are two major solutions. One is the pure data-driven diagnosis approach, which may be very time-consuming;the other is the rule-based analysis method, which usually requires prior knowledge. For Big Data applications like Spark workloads, we observe that the tasks in the same stages normally execute the same or similar codes on each data partition. On basis of the stage similarity and distributed characteristics of Big Data systems, we analyze the behaviors of the Big Data applications in terms of both system and micro-architectural metrics of each stage. Furthermore, for different performance problems, we propose a hybrid approach that combines prior rules and machine learning algorithms to detect performance anomalies, such as straggler tasks, task assignment imbalance, data skew, abnormal nodes and outlier metrics. Following this methodology, we design and implement a lightweight, extensible tool, named HybridTune, and measure the overhead and anomaly detection effectiveness of HybridTune using the BigDataBench benchmarks. Our experiments show that the overhead of HybridTune is only 5%, and the accuracy of outlier detection algorithm reaches up to 93%. Finally, we report several use cases diagnosing Spark and Hadoop workloads using BigDataBench, which demonstrates the potential use of HybridTune.

位置轨迹相关性差分隐私保护技术研究与进展

随着移动互联网、智能终端设备和GPS定位技术的普及应用,产生了大量轨迹数据以应用于基于位置的服务.然而,具有时空相关性的行动轨迹可以体现或泄露单用户的个人信息或多用户之间的某些社会关系信息,进一步导致严重的隐私泄露.这些位置轨迹数据的共享和发布离不开隐私保护技术的支持,而大多数隐私保护模型的安全性是基于一定的背景知识,若背景知识已被攻击者掌握,则无法提供完善的隐私保护.差分隐私技术有效弥补这一缺陷,并广泛应用于位置轨迹数据相关性隐私保护领域中.本文首先介绍差分隐私、全局敏感度、噪声机制以及组合性质等相关概念;其次系统地对基于差分隐私的位置轨迹相关性隐私保护技术进行了研究与分析,进一步地详细阐述了差分隐私模型下单条轨迹内相关性保护、两条不同轨迹之间相关性保护以及多条不同轨迹之间相关性保护在时间、空间以及时空方面的隐私保护方法;最后,在对现有方法对比分析的基础上,提出了位置轨迹相关性隐私保护未来的重点研究发展方向.

瓦里关地区大气降水化学特征及离子来源

基于瓦里关地区2011—2019年采集的604个降水样品,分析了降水的pH值、电导率及离子浓度特征,并应用因子分析、富集因子、相关分析及后向轨迹法,讨论了降水化学特征和离子来源。结果表明:1)瓦里关地区降水pH值变化范围为4.37~8.13,加权平均值为6.13;2)电导率变化范围为2.47~153.2μs/cm,加权平均值为14.87μs/cm;3)离子总浓度变化范围为21.46~2027.55μeq/L,加权平均总浓度为232.05μeq/L,各离子加权平均浓度大小顺序为Ca2+>NH4+>SO42->NO3->Cl->Na+Mg2+>F->K+;Ca2+是降水中主要的阳离子,其浓度为89.73μeq/L,对离子总浓度的贡献率为3.9%,SO42-为降水中主要的阴离子,其浓度为3.8.34μeq/L,对离子总浓度的贡献率为17%;瓦里关地区降水中各离子浓度除NH4+外,均表现出冬春季偏高,夏春季较低的趋势;4)中和因子分析表明,Ca2+、NH4+对降水酸性的中和起主导作用;5)海盐示踪法和相关性分析显示,研究区降水离子主要来自陆源贡献,Na+与Cl-基本来源于海源,Ca2+、NO3-和SO42-基本由陆源贡献,海源和陆源对K+、Mg2+都有一定的贡献;6)后向轨迹分析显示,瓦里关地区降水离子浓度主要受西风源和局地源影响。

2014-2021年德阳大气颗粒污染物特征及案例分析

利用德阳2014—2021年气象观测数据和大气颗粒污染物数据,分析了PM_(2.5)和PM_(10)浓度变化特征及其与气象要素相关性,并以2017年1月25—28日PM_(2.5)污染过程为案例,开展了污染过程的气象条件和后向轨迹分析。结果表明:(1)2014—2021年德阳PM_(2.5)和PM_(10)浓度呈下降趋势,二者浓度的日最大值、最小值分别出现在11时、17时,月均浓度都表现出“夏低冬高”的特征。(2)德阳四季PM_(2.5)和PM_(10)日均浓度均与降水量及地面风速呈显著负相关关系,而与相对湿度、气温、气压的相关性则存在季节差异。(3)地面均压场、气压梯度小、风力微弱的静稳天气条件以及上游污染物的输入是导致此次重污染形成和加重的主要因素。

Optimizing segmented trajectory data storage with HBase for improved spatio-temporal query efficiency

The surging accumulation of trajectory data has yielded invaluable insights into urban systems,but it has also presented challenges for data storage and management systems.In response,specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches.However,these systems often utilize storage by point or storage by trajectory methods,both of which have drawbacks.In this study,we evaluate the effectiveness of segmented trajectory data storage with HBase optimizations for spatio-temporal queries.We develop a prototype system that includes trajectory segmentation,serialization,and spatio-temporal indexing and apply it to taxi trajectory data in Beijing.Ourfindings indicate that the segmented system provides enhanced query speed and reduced memory usage compared to the Geomesa system.

基于毫米波雷达点云的路口车辆轨迹跟踪方法

毫米波雷达作为一种新兴的交通检测设备,受光照、天气等环境影响较小,能为道路交通感知、安全管控和信号配时优化等方面提供可靠的数据支撑。毫米波雷达获取的车辆轨迹数据蕴含着丰富的交通信息,反映了车辆时空运动特征,对交通参数提取、事件检测、驾驶行为分析、信号配时优化等具有重要意义。针对路口毫米波雷达检测车辆时数据丢失、易被遮挡,导致轨迹碎片化、有效跟踪率低等问题,文章采用多阶段关联的思想提出了一种基于短轨迹片段关联的车辆轨迹连续跟踪方法。首先,获取路口处毫米波雷达高频采集的二维点云数据,并进行清洗获取有效目标点云;其次,通过帧间关联从二维点云中提取短轨迹片段,并使用多重运动序列特征进行轨迹片段修正以剔除分裂轨迹;第三,基于时空维度的运动特性构建模糊相关函数描述车辆经过路口时多个短轨迹碎片之间的相关度,并利用匈牙利法求解关联度最高的构成目标短轨迹集合;最后,基于三次分段Hermite插值法修复短轨迹集合中缺失的轨迹点,推算完整轨迹并实现连续跟踪。利用真实路口采集的6627帧二维点云数据进行试验,其结果表明:相较于传统轨迹跟踪算法,提出的方法在不同车流密度、监测方向和遮挡情况下均能取得较优的跟踪性能,其跟踪准确性为92.4%,平均轨迹间断数为4.5条,估计的车辆数准确率显著提升。

非合作双基地雷达弱目标KCF⁃TBD方法

非合作双基地雷达因其特殊的探测方式,致使回波中目标信噪比较低,雷达扫描周期的帧与帧之间探测并不稳定,因此传统的先检测后跟踪(Detect⁃Before⁃Track,DBT)在这种场景下并不适用。本文利用动态规划检测前跟踪(Dynamic Programming Track⁃Before⁃Detect,DP⁃TBD)值函数累积的思想,结合现用于视觉跟踪中的核相关滤波器(Kernelized Correlation Filters,KCF),将峰值旁瓣比(Peak Sidelobe Ratio,PSR)作为值函数,提出了KCF⁃TBD,并采用深度学习技术对跟踪框进行修正,以解决实际目标跟踪中出现的漂移、丢失等问题,从而提高跟踪过程的稳定性和适应性。在实际的海面目标数据上的验证结果显示,所提算法具有较高的跟踪稳定性和适应性。